【正文】 and a separate arm control system puter that performs inverse kinematic transformations and jointlevel servicing. Each of these systems has its own sampling rate, noise characteristics, and processing delays, which need to be integrated to achieve smooth and stable realtime performance. In our case, this involves overing visual processing noise and delays with a predictive filter based upon a probabilistic analysis of the system noise characteristics. In addition, realtime arm control needs to be able to operate at fast servo rates regardless of whether new 安徽建筑工業(yè)學(xué)院 畢業(yè)設(shè)計（論文） 34 predictions of object position are available. The system consists of two fixed cameras that can image a scene containing a moving object (Fig. 1). A PUMA560 with a parallel jaw gripper attached is used to track and pick up the object as it moves (Fig. 2). The system operates as follows: 1) The imaging system performs a stereoscopic opticflow calculation at each pixel in the image. From these opticflow fields, a motion energy profile is obtained that forms the basis for a triangulation that can recover the 3D position of a moving object at video rates. 2) The 3D position of the moving object puted by step 1 is initially smoothed to remove sensor noise, and a nonlinear filter is used to recover the correct trajectory parameters which can be used for forward prediction, and the updated position is sent to the trajectoryplanner/armcontrol system. 3) The trajectory planner updates the jointlevel servos of the arm via kinematic transform equations. An additional fixedgain filter is used to provide servolevel control in case of missed or delayed munication from the vision and filtering system. 4) Once tracking is stable, the system mands the arm to intercept the moving object and the hand is used to grasp the object stably and pick it up. The following sections of the paper describe each of these subsystems in detail along with experimental results. П . PREVIOUS WORK Previous efforts in the areas of motion tracking and realtime control are too numerous to exhaustively list here. We instead list some notable efforts that have inspired us to use similar approaches. Burt et al. [9] have focused on highspeed feature detection and hierarchical scaling of images in order to meet the realtime demands of surveillance and other robotic applications. Related work has been reported by. Lee and Wohn [29] and Wiklund and Granlund [43] who use。 integration of systems with different sampling and processing rates. Most plex robotic systems are actually amalgams of different processing devices, connected by a variety of methods. For example, our system consists of three separate putation systems: a parallel image processing puter。s sensing modalities and motor control system is a hallmark of intelligent behavior, and we are pursuing the goal of building an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system we have built addresses three distinct problems in robotic handeye coordination for grasping moving objects: fast putation of 3D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system is able to operate at approximately human arm movement rates, and experimental results in which a moving model train is tracked is presented, stably grasped, and picked up by the system. The algorithms we have developed that relate sensing to actuation are quite general and applicable to a variety of plex robotic tasks that require visual feedback for arm and hand control. I. INTRODUCTION The focus of our work is to achieve a high level of interaction between realtime vision systems capable of tracking moving objects in 3D and a robot arm equipped with a dexterous hand that can be used to intercept, grasp, and pick up a moving object. We are interested in exploring the interplay of handeye coordination for dynamic grasping tasks such as grasping of parts on a moving conveyor system, 安徽建筑工業(yè)學(xué)院 畢業(yè)設(shè)計（論文） 33 assembly of articulated parts, or for grasping from a mobile robotic system. Coordination between an anism39。 最后感謝 機械與電氣工程學(xué) 院和我的母校 —安徽建筑工業(yè)學(xué)院 四年來對我的大力栽培。此次畢業(yè)設(shè)計才會順利完成。 如果 我 們之間的相互幫助 ，此次設(shè)計的完成將變得非常困難。除了敬佩 黃 老師的專業(yè)水平外，他的治學(xué)嚴謹和科學(xué)研究的精神也是我永遠學(xué)習(xí)的榜樣，并將積極影響我今后的學(xué)習(xí)和工作。 黃 老師平日里工作繁多，但在我做畢業(yè)設(shè)計的每個階段，從查閱資料，設(shè)計草案的確定和修改，中期檢查 答辯 ，后期詳細設(shè)計，裝配草圖等整個過程中都給予了我悉心的指導(dǎo)。 安徽建筑工業(yè)學(xué)院 畢業(yè)設(shè)計（論文） 30 謝 辭 經(jīng)過半年的忙碌和工作，本次畢業(yè)設(shè)計已經(jīng)接近尾聲，作為一個本科生的畢業(yè)設(shè)計，由于 所學(xué)知識有限， 經(jīng)驗的匱乏，難免有許多考慮不周全的地方，如果沒有導(dǎo)師的督促指導(dǎo) ，以及同學(xué)們的支持，想要完成這個設(shè)計是難以想象的。 隨著科技和社會的進步，智能機器人在人們生活的各個領(lǐng)域發(fā)揮著越來越大的作用。 2) 系 統(tǒng)幾個主要模塊尚未進行過實際考核，在工作可靠性、抗干擾性能等方面有待進一步完善和提高。 3) 通過對各個典型機構(gòu)的設(shè)計，充分的理解和掌握了機械設(shè)計方面的知識，并且也對專業(yè)上的智能控制和誤差控制方面有了更加深刻的認識?；仡欉^去的兩個多月，感覺收獲頗豐： 1) 通過對機械手的整體方案設(shè)計，典型結(jié)構(gòu)設(shè)計，使我對大學(xué)四年所學(xué)的機械方面的知識以及專業(yè)方面的知識有了更深一步的了解和認識，而不像以前一樣僅僅停留在書本的概念上。 安徽建筑工業(yè)學(xué)院 畢業(yè)設(shè)計（論文） 29 5 總結(jié)與展望 歷經(jīng)一個學(xué)期的 努力， 六自由度機械手 終于 設(shè)計成功。i =18 查表得： 蝸桿頭數(shù) 1Z =2 ? 39。i =30 1i = ? 帶輪的主動論轉(zhuǎn)速為 1n =3r/min 由式 1160dnV ?? V 一般在 5—— 25m/s 取 V=20m/s ?1 60 20 4 3d ???? 查表得：選帶輪為 Y 型帶 1d =20 又 2i =1 2 20d?? ? ? ? ?1 2 0 1 20 . 7 2d d a d d? ? ? ? ? 取 ? ?0 1 21 .5 6 0a d d? ? ? ? V 帶基準長度 ? ? ? ? 2210 0 1 202 1 8 2 .824 ddL a d d a? ?? ? ? ? ? 查表得： 取 dL =200 ? 00 ?? ? ? [8] 手腕擺動電機處蝸輪蝸桿、帶傳動比的確定 由手腕擺動處選用電機為 36BF003 型可得，運行頻率為 f=27000HZ ? mn =又 sn =5r/min ? 總傳動比 i= msnn = 又 大手臂擺動電機確定的一級帶輪傳動比為 1i = 小手臂擺動電機確定的二級帶輪傳動比為 2i =1 取三級傳動比為 3i =1 ? 蝸輪蝸桿的傳動比為 39。i =30 查表得： 蝸桿頭數(shù) 1Z =2 ? 39。 小手臂電機處蝸輪蝸桿、帶傳動比的確定 由小手臂擺動選用電機為 45BF003 型可得，運行頻率為 f=27000HZ ? mn =又 sn =3r/min ? 總傳動比 i= msnn = 由大手臂擺動電機確定的一級帶輪傳動比為 1i = 取二級帶輪傳動比為 2i =1 ? 小手臂擺動電機蝸輪蝸桿的傳動比為 39。 則手腕部分折算到外軸上的轉(zhuǎn)動慣量為 2 22. 3 .